Journal articles: 'Document-level relation extraction'

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Li, Jian, Ruijuan Hu, Keliang Zhang, Haiyan Liu, and Yanzhou Ma. "DEERE: Document-Level Event Extraction as Relation Extraction." Mobile Information Systems 2022 (August 26, 2022): 1–8. http://dx.doi.org/10.1155/2022/2742796.

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The descriptions of complex events usually span sentences, so we need to extract complete event information from the whole document. To address the challenges of document-level event extraction, we propose a novel framework named Document-level Event Extraction as Relation Extraction (DEERE), which is suitable for document-level event extraction tasks without trigger-word labelling. By well-designed task transformation, DEERE remodels event extraction as single-stage relation extraction, which can mitigate error propagation. A long text supported encoder is adopted in the relation extraction model to aware the global context effectively. A fault-tolerant event integration algorithm is designed to improve the prediction accuracy. Experimental results show that our approach advances the SOTA for the ChFinAnn dataset by an average F1-score of 3.7. The code and data are available at https://github.com/maomaotfntfn/DEERE.

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Xu, Wang, Kehai Chen, and Tiejun Zhao. "Document-Level Relation Extraction with Reconstruction." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 16 (2021): 14167–75. http://dx.doi.org/10.1609/aaai.v35i16.17667.

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In document-level relation extraction (DocRE), graph structure is generally used to encode relation information in the input document to classify the relation category between each entity pair, and has greatly advanced the DocRE task over the past several years. However, the learned graph representation universally models relation information between all entity pairs regardless of whether there are relationships between these entity pairs. Thus, those entity pairs without relationships disperse the attention of the encoder-classifier DocRE for ones with relationships, which may further hind the improvement of DocRE. To alleviate this issue, we propose a novel encoder-classifier-reconstructor model for DocRE. The reconstructor manages to reconstruct the ground-truth path dependencies from the graph representation, to ensure that the proposed DocRE model pays more attention to encode entity pairs with relationships in the training. Furthermore, the reconstructor is regarded as a relationship indicator to assist relation classification in the inference, which can further improve the performance of DocRE model. Experimental results on a large-scale DocRE dataset show that the proposed model can significantly improve the accuracy of relation extraction on a strong heterogeneous graph-based baseline. The code is publicly available at https://github.com/xwjim/DocRE-Rec.

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Liu, Zishu, Yongquan Liang, and Weijian Ni. "Document-Level Causal Event Extraction Enhanced by Temporal Relations Using Dual-Channel Neural Network." Electronics 14, no. 5 (2025): 992. https://doi.org/10.3390/electronics14050992.

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Event–event causal relation extraction (ECRE) represents a critical yet challenging task in natural language processing. Existing studies primarily focus on extracting causal sentences and events, despite the use of joint extraction methods for both tasks. However, both pipeline methods and joint extraction approaches often overlook the impact of document-level event temporal sequences on causal relations. To address this limitation, we propose a model that incorporates document-level event temporal order information to enhance the extraction of implicit causal relations between events. The proposed model comprises two channels: an event–event causal relation extraction channel (ECC) and an event–event temporal relation extraction channel (ETC). Temporal features provide critical support for modeling node weights in the causal graph, thereby improving the accuracy of causal reasoning. An Association Link Network (ALN) is introduced to construct an Event Causality Graph (ECG), incorporating an innovative design that computes node weights using Kullback–Leibler divergence and Gaussian kernels. The experimental results indicate that our model significantly outperforms baseline models in terms of accuracy and weighted average F1 scores.

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Duan, Zhichao, Tengyu Pan, Zhenyu Li, Xiuxing Li, and Jianyong Wang. "COMM: Concentrated Margin Maximization for Robust Document-Level Relation Extraction." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 22 (2025): 23841–49. https://doi.org/10.1609/aaai.v39i22.34556.

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Document-level relation extraction (DocRE) is the process of identifying and extracting relations between entities that span multiple sentences within a document. Due to its realistic settings, DocRE has garnered increasing research attention in recent years. Previous research has mostly focused on developing sophisticated encoding models to better capture the intricate patterns between entity pairs. While these advancements are undoubtedly crucial, an even more foundational challenge lies in the data itself. The complexity inherent in DocRE makes the labeling process prone to errors, compounded by the extreme sparsity of positive relation samples, which is driven by both the limited availability of positive instances and the broad diversity of positive relation types. These factors can lead to biased optimization processes, further complicating the task of accurate relation extraction. Recognizing these challenges, we have developed a robust framework called COMM to better solve DocRE. COMM operates by initially employing an instance-aware reasoning method to dynamically capture pertinent information of entity pairs within the document and extract relational features. Following this, COMM takes into account the distribution of relations and the difficulty of samples to dynamically adjust the margins between prediction logits and the decision threshold, a process we call Concentrated Margin Maximization. In this way, COMM not only enhances the extraction of relevant relational features but also boosts DocRE performance by addressing the specific challenges posed by the data. Extensive experiments and analysis demonstrate the versatility and effectiveness of COMM, especially its robustness when trained on low-quality data (achieves >10% performance gains).

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Wu, Hao, Gang Zhou, Yi Xia, Hongbo Liu, and Tianzhi Zhang. "Self-distillation framework for document-level relation extraction in low-resource environments." PeerJ Computer Science 10 (March 29, 2024): e1930. http://dx.doi.org/10.7717/peerj-cs.1930.

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The objective of document-level relation extraction is to retrieve the relations existing between entities within a document. Currently, deep learning methods have demonstrated superior performance in document-level relation extraction tasks. However, to enhance the model’s performance, various methods directly introduce additional modules into the backbone model, which often increases the number of parameters in the overall model. Consequently, deploying these deep models in resource-limited environments presents a challenge. In this article, we introduce a self-distillation framework for document-level relational extraction. We partition the document-level relation extraction model into two distinct modules, namely, the entity embedding representation module and the entity pair embedding representation module. Subsequently, we apply separate distillation techniques to each module to reduce the model’s size. In order to evaluate the proposed framework’s performance, two benchmark datasets for document-level relation extraction, namely GDA and DocRED are used in this study. The results demonstrate that our model effectively enhances performance and significantly reduces the model’s size.

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Liu, Yiming, Hongtao Shan, Feng Nie, Gaoyu Zhang, and George Xianzhi Yuan. "Document-Level Relation Extraction with Local Relation and Global Inference." Information 14, no. 7 (2023): 365. http://dx.doi.org/10.3390/info14070365.

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The current popular approach to the extraction of document-level relations is mainly based on either a graph structure or serialization model method for the inference, but the graph structure method makes the model complicated, while the serialization model method decreases the extraction accuracy as the text length increases. To address such problems, the goal of this paper is to develop a new approach for document-level relationship extraction by applying a new idea through the consideration of so-called “Local Relationship and Global Inference” (in short, LRGI), which means that we first encode the text using the BERT pre-training model to obtain a local relationship vector first by considering a local context pooling and bilinear group algorithm and then establishing a global inference mechanism based on Floyd’s algorithm to achieve multi-path multi-hop inference and obtain the global inference vector, which allow us to extract multi-classified relationships with adaptive thresholding criteria. Taking the DocRED dataset as a testing set, the numerical results show that our proposed new approach (LRGI) in this paper achieves an accuracy of 0.73, and the value of F1 is 62.11, corresponding to 28% and 2% improvements by comparing with the classical document-level relationship extraction model (ATLOP), respectively.

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Liu, Xiaofeng, Jianye Fan, and Shoubin Dong. "Document-Level Biomedical Relation Extraction Leveraging Pretrained Self-Attention Structure and Entity Replacement: Algorithm and Pretreatment Method Validation Study." JMIR Medical Informatics 8, no. 5 (2020): e17644. http://dx.doi.org/10.2196/17644.

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Background The most current methods applied for intrasentence relation extraction in the biomedical literature are inadequate for document-level relation extraction, in which the relationship may cross sentence boundaries. Hence, some approaches have been proposed to extract relations by splitting the document-level datasets through heuristic rules and learning methods. However, these approaches may introduce additional noise and do not really solve the problem of intersentence relation extraction. It is challenging to avoid noise and extract cross-sentence relations. Objective This study aimed to avoid errors by dividing the document-level dataset, verify that a self-attention structure can extract biomedical relations in a document with long-distance dependencies and complex semantics, and discuss the relative benefits of different entity pretreatment methods for biomedical relation extraction. Methods This paper proposes a new data preprocessing method and attempts to apply a pretrained self-attention structure for document biomedical relation extraction with an entity replacement method to capture very long-distance dependencies and complex semantics. Results Compared with state-of-the-art approaches, our method greatly improved the precision. The results show that our approach increases the F1 value, compared with state-of-the-art methods. Through experiments of biomedical entity pretreatments, we found that a model using an entity replacement method can improve performance. Conclusions When considering all target entity pairs as a whole in the document-level dataset, a pretrained self-attention structure is suitable to capture very long-distance dependencies and learn the textual context and complicated semantics. A replacement method for biomedical entities is conducive to biomedical relation extraction, especially to document-level relation extraction.

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Chen, Mei, Tingting Zhang, and Shibin Wang. "Prompting large language models to extract chemical‒disease relation precisely and comprehensively at the document level: an evaluation study." PLOS ONE 20, no. 4 (2025): e0320123. https://doi.org/10.1371/journal.pone.0320123.

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Given the scarcity of annotated data, current deep learning methods face challenges in the field of document-level chemical-disease relation extraction, making it difficult to achieve precise relation extraction capable of identifying relation types and comprehensive extraction tasks that identify relation-related factors. This study tests the abilities of three large language models (LLMs), GPT3.5, GPT4.0, and Claude-opus, to perform precise and comprehensive extraction in document-level chemical-disease relation extraction on a self-constructed dataset. Firstly, based on the task characteristics, this study designs six workflows for precise extraction and five workflows for comprehensive extraction using prompting engineering strategies. The characteristics of the extraction process are analyzed through the performance differences under different workflows. Secondly, this study analyzes the content bias in LLMs extraction by examining the extraction effectiveness of different workflows on different types of content. Finally, this study analyzes the error characteristics of extracting incorrect examples by the LLMs. The experimental results show that: (1) The LLMs demonstrate good extraction capabilities, achieving the highest F1 scores of 87% and 73% respectively in the tasks of precise extraction and comprehensive extraction; (2) In the extraction process, the LLMs exhibit a certain degree of stubbornness, with limited effectiveness of prompting engineering strategies; (3) In terms of extraction content, the LLMs show a content bias, with stronger abilities to identify positive relations such as induction and acceleration; (4) The essence of extraction errors lies in the LLMs’ misunderstanding of the implicit meanings in biomedical texts. This study provides practical workflows for precise and comprehensive extraction of document-level chemical-disease relations and also indicates that optimizing training data is the key to building more efficient and accurate extraction methods in the future.

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Yuan, Changsen, Heyan Huang, Chong Feng, Ge Shi, and Xiaochi Wei. "Document-level relation extraction with Entity-Selection Attention." Information Sciences 568 (August 2021): 163–74. http://dx.doi.org/10.1016/j.ins.2021.04.007.

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Kong, Lingxing, Jiuliang Wang, Zheng Ma, et al. "A Hierarchical Network for Multimodal Document-Level Relation Extraction." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 16 (2024): 18408–16. http://dx.doi.org/10.1609/aaai.v38i16.29801.

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Document-level relation extraction aims to extract entity relations that span across multiple sentences. This task faces two critical issues: long dependency and mention selection. Prior works address the above problems from the textual perspective, however, it is hard to handle these problems solely based on text information. In this paper, we leverage video information to provide additional evidence for understanding long dependencies and offer a wider perspective for identifying relevant mentions, thus giving rise to a new task named Multimodal Document-level Relation Extraction (MDocRE). To tackle this new task, we construct a human-annotated dataset including documents and relevant videos, which, to the best of our knowledge, is the first document-level relation extraction dataset equipped with video clips. We also propose a hierarchical framework to learn interactions between different dependency levels and a textual-guided transformer architecture that incorporates both textual and video modalities. In addition, we utilize a mention gate module to address the mention-selection problem in both modalities. Experiments on our proposed dataset show that 1) incorporating video information greatly improves model performance; 2) our hierarchical framework has state-of-the-art results compared with both unimodal and multimodal baselines; 3) through collaborating with video information, our model better solves the long-dependency and mention-selection problems.

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Chen, Yang, and Bowen Shi. "Enhanced Heterogeneous Graph Attention Network with a Novel Multilabel Focal Loss for Document-Level Relation Extraction." Entropy 26, no. 3 (2024): 210. http://dx.doi.org/10.3390/e26030210.

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Recent years have seen a rise in interest in document-level relation extraction, which is defined as extracting all relations between entities in multiple sentences of a document. Typically, there are multiple mentions corresponding to a single entity in this context. Previous research predominantly employed a holistic representation for each entity to predict relations, but this approach often overlooks valuable information contained in fine-grained entity mentions. We contend that relation prediction and inference should be grounded in specific entity mentions rather than abstract entity concepts. To address this, our paper proposes a two-stage mention-level framework based on an enhanced heterogeneous graph attention network for document-level relation extraction. Our framework employs two different strategies to model intra-sentential and inter-sentential relations between fine-grained entity mentions, yielding local mention representations for intra-sentential relation prediction and global mention representations for inter-sentential relation prediction. For inter-sentential relation prediction and inference, we propose an enhanced heterogeneous graph attention network to better model the long-distance semantic relationships and design an entity-coreference path-based inference strategy to conduct relation inference. Moreover, we introduce a novel cross-entropy-based multilabel focal loss function to address the class imbalance problem and multilabel prediction simultaneously. Comprehensive experiments have been conducted to verify the effectiveness of our framework. Experimental results show that our approach significantly outperforms the existing methods.

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Makino, Kohei, Makoto Miwa, and Yutaka Sasaki. "Editing Relation Candidate Edges of Relation Graphs for Document-Level Relation Extraction." Journal of Natural Language Processing 30, no. 2 (2023): 557–85. http://dx.doi.org/10.5715/jnlp.30.557.

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Wang, Jian, Xiaoyu Chen, Yu Zhang, et al. "Document-Level Biomedical Relation Extraction Using Graph Convolutional Network and Multihead Attention: Algorithm Development and Validation." JMIR Medical Informatics 8, no. 7 (2020): e17638. http://dx.doi.org/10.2196/17638.

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Background Automatically extracting relations between chemicals and diseases plays an important role in biomedical text mining. Chemical-disease relation (CDR) extraction aims at extracting complex semantic relationships between entities in documents, which contain intrasentence and intersentence relations. Most previous methods did not consider dependency syntactic information across the sentences, which are very valuable for the relations extraction task, in particular, for extracting the intersentence relations accurately. Objective In this paper, we propose a novel end-to-end neural network based on the graph convolutional network (GCN) and multihead attention, which makes use of the dependency syntactic information across the sentences to improve CDR extraction task. Methods To improve the performance of intersentence relation extraction, we constructed a document-level dependency graph to capture the dependency syntactic information across sentences. GCN is applied to capture the feature representation of the document-level dependency graph. The multihead attention mechanism is employed to learn the relatively important context features from different semantic subspaces. To enhance the input representation, the deep context representation is used in our model instead of traditional word embedding. Results We evaluate our method on CDR corpus. The experimental results show that our method achieves an F-measure of 63.5%, which is superior to other state-of-the-art methods. In the intrasentence level, our method achieves a precision, recall, and F-measure of 59.1%, 81.5%, and 68.5%, respectively. In the intersentence level, our method achieves a precision, recall, and F-measure of 47.8%, 52.2%, and 49.9%, respectively. Conclusions The GCN model can effectively exploit the across sentence dependency information to improve the performance of intersentence CDR extraction. Both the deep context representation and multihead attention are helpful in the CDR extraction task.

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Yuan, Changsen, Yixin Cao, and Heyan Huang. "Collective prompt tuning with relation inference for document-level relation extraction." Information Processing & Management 60, no. 5 (2023): 103451. http://dx.doi.org/10.1016/j.ipm.2023.103451.

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Wang, Peng, Zhenkai Deng, and Ruilong Cui. "TDJEE: A Document-Level Joint Model for Financial Event Extraction." Electronics 10, no. 7 (2021): 824. http://dx.doi.org/10.3390/electronics10070824.

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Extracting financial events from numerous financial announcements is very important for investors to make right decisions. However, it is still challenging that event arguments always scatter in multiple sentences in a financial announcement, while most existing event extraction models only work in sentence-level scenarios. To address this problem, this paper proposes a relation-aware Transformer-based Document-level Joint Event Extraction model (TDJEE), which encodes relations between words into the context and leverages modified Transformer to capture document-level information to fill event arguments. Meanwhile, the absence of labeled data in financial domain could lead models be unstable in extraction results, which is known as the cold start problem. Furthermore, a Fonduer-based knowledge base combined with the distant supervision method is proposed to simplify the event labeling and provide high quality labeled training corpus for model training and evaluating. Experimental results on real-world Chinese financial announcement show that, compared with other models, TDJEE achieves competitive results and can effectively extract event arguments across multiple sentences.

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Hou, Wenlong, Wenda Wu, Xianhui Liu, and Weidong Zhao. "Document-level relation extraction with multi-semantic knowledge interaction." Information Sciences 679 (September 2024): 121083. http://dx.doi.org/10.1016/j.ins.2024.121083.

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Wang, Hailin, Ke Qin, Guiduo Duan, and Guangchun Luo. "Denoising Graph Inference Network for Document-Level Relation Extraction." Big Data Mining and Analytics 6, no. 2 (2023): 248–62. http://dx.doi.org/10.26599/bdma.2022.9020051.

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Zhou, Wenxuan, Kevin Huang, Tengyu Ma, and Jing Huang. "Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 16 (2021): 14612–20. http://dx.doi.org/10.1609/aaai.v35i16.17717.

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Document-level relation extraction (RE) poses new challenges compared to its sentence-level counterpart. One document commonly contains multiple entity pairs, and one entity pair occurs multiple times in the document associated with multiple possible relations. In this paper, we propose two novel techniques, adaptive thresholding and localized context pooling, to solve the multi-label and multi-entity problems. The adaptive thresholding replaces the global threshold for multi-label classification in the prior work with a learnable entities-dependent threshold. The localized context pooling directly transfers attention from pre-trained language models to locate relevant context that is useful to decide the relation. We experiment on three document-level RE benchmark datasets: DocRED, a recently released large-scale RE dataset, and two datasets CDRand GDA in the biomedical domain. Our ATLOP (Adaptive Thresholding and Localized cOntext Pooling) model achieves an F1 score of 63.4, and also significantly outperforms existing models on both CDR and GDA. We have released our code at https://github.com/wzhouad/ATLOP.

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Yang, Hao, Qiming Fu, You Lu, Yunzhe Wang, Lanhui Liu, and Jianping Chen. "Document-level multiple relations extraction method via evidence guidance and relation correlation." Applied Soft Computing 167 (December 2024): 112391. http://dx.doi.org/10.1016/j.asoc.2024.112391.

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Jain, Monika, Raghava Mutharaju, Ramakanth Kavuluru, and Kuldeep Singh. "Revisiting Document-Level Relation Extraction with Context-Guided Link Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 16 (2024): 18327–35. http://dx.doi.org/10.1609/aaai.v38i16.29792.

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Document-level relation extraction (DocRE) poses the challenge of identifying relationships between entities within a document. Existing approaches rely on logical reasoning or contextual cues from entities. This paper reframes document-level RE as link prediction over a Knowledge Graph (KG) with distinct benefits: 1) Our approach amalgamates entity context and document-derived logical reasoning, enhancing link prediction quality. 2) Predicted links between entities offer interpretability, elucidating employed reasoning. We evaluate our approach on benchmark datasets - DocRED, ReDocRED, and DWIE. The results indicate that our proposed method outperforms the state-of-the-art models and suggests that incorporating context-based Knowledge Graph link prediction techniques can enhance the performance of document-level relation extraction models.

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Wang, Hailin, Ke Qin, Guoming Lu, Jin Yin, Rufai Yusuf Zakari, and Jim Wilson Owusu. "Document-level relation extraction using evidence reasoning on RST-GRAPH." Knowledge-Based Systems 228 (September 2021): 107274. http://dx.doi.org/10.1016/j.knosys.2021.107274.

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Li, Rongzhen, Jiang Zhong, Zhongxuan Xue, Qizhu Dai, and Xue Li. "Self-supervised commonsense knowledge learning for document-level relation extraction." Expert Systems with Applications 250 (September 2024): 123921. http://dx.doi.org/10.1016/j.eswa.2024.123921.

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Pi, Qiankun, Jicang Lu, Taojie Zhu, Yepeng Sun, Shunhang Li, and Jiaxing Guo. "Enhancing cross-evidence reasoning graph for document-level relation extraction." PeerJ Computer Science 10 (June 17, 2024): e2123. http://dx.doi.org/10.7717/peerj-cs.2123.

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The objective of document-level relation extraction (RE) is to identify the semantic connections that exist between named entities present within a document. However, most entities are distributed among different sentences, there is a need for inter-entity relation prediction across sentences. Existing research has focused on framing sentences throughout documents to predict relationships between entities. However, not all sentences play a substantial role in relation extraction, which inevitably introduces noisy information. Based on this phenomenon, we believe that we can extract evidence sentences in advance and use these evidence sentences to construct graphs to mine semantic information between entities. Thus, we present a document-level RE model that leverages an Enhancing Cross-evidence Reasoning Graph (ECRG) for improved performance. Specifically, we design an evidence extraction rule based on center-sentence to pre-extract higher-quality evidence. Then, this evidence is constructed into evidence graphs to mine the connections between mentions within the same evidence. In addition, we construct entity-level graphs by aggregating mentions from the same entities within the evidence graphs, aiming to capture distant interactions between entities. Experiments result on both DocRED and RE-DocRED datasets demonstrate that our model improves entity RE performance compared to existing work.

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Zhong, Yu, Bo Shen, Tao Wang, Jinglin Zhang, and Yun Liu. "Interaction and Fusion of Rich Textual Information Network for Document-level Relation Extraction." JUCS - Journal of Universal Computer Science 30, no. (8) (2024): 1112–36. https://doi.org/10.3897/jucs.130588.

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Detecting relations between entities across multiple sentences in a document, referred to as document-level relation extraction, poses a challenge in natural language processing. Graph networks have gained widespread application for their ability to capture long-range contextual dependencies in documents. However, previous studies have often been limited to using only two to three types of nodes to construct document graphs. This leads to insufficient utilization of the rich information within the documents and inadequate aggregation of contextual information. Additionally, relevant relationship labels often co-occur in documents, yet existing methods rarely model the dependencies of relationship labels. In this paper, we propose the Interaction and Fusion of Rich Textual Information Network (IFRTIN) that simultaneously considers multiple types of nodes. First, we utilize the structural, syntactic, and discourse information in the document to construct a document graph, capturing global dependency relationships. Next, we design a regularizer to encourage the model to capture dependencies of relationship labels. Furthermore, we design an Adaptive Encouraging Loss, which encourages well-classified instances to contribute more to the overall loss, thereby enhancing the effectiveness of the model. Experimental results demonstrate that our approach achieves a significant improvement on three document-level relation extraction datasets. Specifically, IFRTIN outperforms existing models by achieving an F1 score improvement of 0.67% on Dataset DocRED, 1.2% on Dataset CDR, and 1.3% on Dataset GDA. These results highlight the effectiveness of our approach in leveraging rich textual information and modeling label dependencies for document-level relation extraction.

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Zhong, Yu, Bo Shen, Tao Wang, Jinglin Zhang, and Yun Liu. "Interaction and Fusion of Rich Textual Information Network for Document-level Relation Extraction." JUCS - Journal of Universal Computer Science 30, no. 8 (2024): 1112–36. http://dx.doi.org/10.3897/jucs.130588.

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Detecting relations between entities across multiple sentences in a document, referred to as document-level relation extraction, poses a challenge in natural language processing. Graph networks have gained widespread application for their ability to capture long-range contextual dependencies in documents. However, previous studies have often been limited to using only two to three types of nodes to construct document graphs. This leads to insufficient utilization of the rich information within the documents and inadequate aggregation of contextual information. Additionally, relevant relationship labels often co-occur in documents, yet existing methods rarely model the dependencies of relationship labels. In this paper, we propose the Interaction and Fusion of Rich Textual Information Network (IFRTIN) that simultaneously considers multiple types of nodes. First, we utilize the structural, syntactic, and discourse information in the document to construct a document graph, capturing global dependency relationships. Next, we design a regularizer to encourage the model to capture dependencies of relationship labels. Furthermore, we design an Adaptive Encouraging Loss, which encourages well-classified instances to contribute more to the overall loss, thereby enhancing the effectiveness of the model. Experimental results demonstrate that our approach achieves a significant improvement on three document-level relation extraction datasets. Specifically, IFRTIN outperforms existing models by achieving an F1 score improvement of 0.67% on Dataset DocRED, 1.2% on Dataset CDR, and 1.3% on Dataset GDA. These results highlight the effectiveness of our approach in leveraging rich textual information and modeling label dependencies for document-level relation extraction.

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Kim, Kuekyeng, Yuna Hur, Gyeongmin Kim, and Heuiseok Lim. "GREG: A Global Level Relation Extraction with Knowledge Graph Embedding." Applied Sciences 10, no. 3 (2020): 1181. http://dx.doi.org/10.3390/app10031181.

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In an age overflowing with information, the task of converting unstructured data into structured data are a vital task of great need. Currently, most relation extraction modules are more focused on the extraction of local mention-level relations—usually from short volumes of text. However, in most cases, the most vital and important relations are those that are described in length and detail. In this research, we propose GREG: A Global level Relation Extractor model using knowledge graph embeddings for document-level inputs. The model uses vector representations of mention-level ‘local’ relation’s to construct knowledge graphs that can represent the input document. The knowledge graph is then used to predict global level relations from documents or large bodies of text. The proposed model is largely divided into two modules which are synchronized during their training. Thus, each of the model’s modules is designed to deal with local relations and global relations separately. This allows the model to avoid the problem of struggling against loss of information due to too much information crunched into smaller sized representations when attempting global level relation extraction. Through evaluation, we have shown that the proposed model yields high performances in both predicting global level relations and local level relations consistently.

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Wang, Ye, Huazheng Pan, Tao Zhang, Wen Wu, and Wenxin Hu. "A Positive-Unlabeled Metric Learning Framework for Document-Level Relation Extraction with Incomplete Labeling." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 17 (2024): 19197–205. http://dx.doi.org/10.1609/aaai.v38i17.29888.

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The goal of document-level relation extraction (RE) is to identify relations between entities that span multiple sentences. Recently, incomplete labeling in document-level RE has received increasing attention, and some studies have used methods such as positive-unlabeled learning to tackle this issue, but there is still a lot of room for improvement. Motivated by this, we propose a positive-augmentation and positive-mixup positive-unlabeled metric learning framework (P3M). Specifically, we formulate document-level RE as a metric learning problem. We aim to pull the distance closer between entity pair embedding and their corresponding relation embedding, while pushing it farther away from the none-class relation embedding. Additionally, we adapt the positive-unlabeled learning to this loss objective. In order to improve the generalizability of the model, we use dropout to augment positive samples and propose a positive-none-class mixup method. Extensive experiments show that P3M improves the F1 score by approximately 4-10 points in document-level RE with incomplete labeling, and achieves state-of-the-art results in fully labeled scenarios. Furthermore, P3M has also demonstrated robustness to prior estimation bias in incomplete labeled scenarios.

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Park, Minjun, Chan Ung Jeong, Young Sang Baik, et al. "SCREENER: Streamlined collaborative learning of NER and RE model for discovering gene-disease relations." PLOS ONE 18, no. 11 (2023): e0294713. http://dx.doi.org/10.1371/journal.pone.0294713.

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Finding relations between genes and diseases is essential in developing a clinical diagnosis, treatment, and drug design for diseases. One successful approach for mining the literature is the document-based relation extraction method. Despite recent advances in document-level extraction of entity-entity, there remains a difficulty in understanding the relations between distant words in a document. To overcome the above limitations, we propose an AI-based text-mining model that learns the document-level relations between genes and diseases using an attention mechanism. Furthermore, we show that including a direct edge (DE) and indirect edges between genetic targets and diseases when training improves the model’s performance. Such relation edges can be visualized as graphs, enhancing the interpretability of the model. For the performance, we achieved an F1-score of 0.875, outperforming state-of-the-art document-level extraction models. In summary, the SCREENER identifies biological connections between target genes and diseases with superior performance by leveraging direct and indirect target-disease relations. Furthermore, we developed a web service platform named SCREENER (Streamlined CollaboRativE lEarning of NEr and Re), which extracts the gene-disease relations from the biomedical literature in real-time. We believe this interactive platform will be useful for users to uncover unknown gene-disease relations in the world of fast-paced literature publications, with sufficient interpretation supported by graph visualizations. The interactive website is available at: https://ican.standigm.com.

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Liu, Xiaofeng, Kaiwen Tan, and Shoubin Dong. "Multi-granularity sequential neural network for document-level biomedical relation extraction." Information Processing & Management 58, no. 6 (2021): 102718. http://dx.doi.org/10.1016/j.ipm.2021.102718.

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Wang, Nianbin, Tiantian Chen, Chaoqi Ren, and Hongbin Wang. "Document-level relation extraction with multi-layer heterogeneous graph attention network." Engineering Applications of Artificial Intelligence 123 (August 2023): 106212. http://dx.doi.org/10.1016/j.engappai.2023.106212.

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Ma, Youmi, An Wang, and Naoaki Okazaki. "Incorporating Evidence Retrieval into Document-Level Relation Extraction by Guiding Attention." Journal of Natural Language Processing 31, no. 1 (2024): 105–33. http://dx.doi.org/10.5715/jnlp.31.105.

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Xu, Benfeng, Quan Wang, Yajuan Lyu, Yong Zhu, and Zhendong Mao. "Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 16 (2021): 14149–57. http://dx.doi.org/10.1609/aaai.v35i16.17665.

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Entities, as the essential elements in relation extraction tasks, exhibit certain structure. In this work, we formulate such entity structure as distinctive dependencies between mention pairs. We then propose SSAN, which incorporates these structural dependencies within the standard self-attention mechanism and throughout the overall encoding stage. Specifically, we design two alternative transformation modules inside each self-attention building block to produce attentive biases so as to adaptively regularize its attention flow. Our experiments demonstrate the usefulness of the proposed entity structure and the effectiveness of SSAN. It significantly outperforms competitive baselines, achieving new state-of-the-art results on three popular document-level relation extraction datasets. We further provide ablation and visualization to show how the entity structure guides the model for better relation extraction. Our code is publicly available.

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Zhang, Liang, Jinsong Su, Zijun Min, et al. "Exploring Self-Distillation Based Relational Reasoning Training for Document-Level Relation Extraction." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (2023): 13967–75. http://dx.doi.org/10.1609/aaai.v37i11.26635.

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Document-level relation extraction (RE) aims to extract relational triples from a document. One of its primary challenges is to predict implicit relations between entities, which are not explicitly expressed in the document but can usually be extracted through relational reasoning. Previous methods mainly implicitly model relational reasoning through the interaction among entities or entity pairs. However, they suffer from two deficiencies: 1) they often consider only one reasoning pattern, of which coverage on relational triples is limited; 2) they do not explicitly model the process of relational reasoning. In this paper, to deal with the first problem, we propose a document-level RE model with a reasoning module that contains a core unit, the reasoning multi-head self-attention unit. This unit is a variant of the conventional multi-head self-attention and utilizes four attention heads to model four common reasoning patterns, respectively, which can cover more relational triples than previous methods. Then, to address the second issue, we propose a self-distillation training framework, which contains two branches sharing parameters. In the first branch, we first randomly mask some entity pair feature vectors in the document, and then train our reasoning module to infer their relations by exploiting the feature information of other related entity pairs. By doing so, we can explicitly model the process of relational reasoning. However, because the additional masking operation is not used during testing, it causes an input gap between training and testing scenarios, which would hurt the model performance. To reduce this gap, we perform conventional supervised training without masking operation in the second branch and utilize Kullback-Leibler divergence loss to minimize the difference between the predictions of the two branches. Finally, we conduct comprehensive experiments on three benchmark datasets, of which experimental results demonstrate that our model consistently outperforms all competitive baselines. Our source code is available at https://github.com/DeepLearnXMU/DocRE-SD

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Wang, Hongbin, Shuning Yu, and Yantuan Xian. "Document-Level Relation Extraction with Uncertainty Pseudo-Label Selection and Hard-Sample Focal Loss." Journal of Advanced Computational Intelligence and Intelligent Informatics 28, no. 2 (2024): 361–70. http://dx.doi.org/10.20965/jaciii.2024.p0361.

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Relation extraction is a fundamental task in natural language processing that aims to identify structured triple relationships from unstructured text. In recent years, research on relation extraction has gradually advanced from the sentence level to the document level. Most existing document-level relation extraction (DocRE) models are fully supervised and their performance is limited by the dataset quality. However, existing DocRE datasets suffer from annotation omission, making fully supervised models unsuitable for real-world scenarios. To address this issue, we propose the DocRE method based on uncertainty pseudo-label selection. This method first trains a teacher model to annotate pseudo-labels for a dataset with incomplete annotations, trains a student model on the dataset with annotated pseudo-labels, and uses the trained student model to predict relations on the test set. To mitigate the confirmation bias problem in pseudo-label methods, we performed adversarial training on the teacher model and calculated the uncertainty of the model output to supervise the generation of pseudo-labels. In addition, to address the hard-easy sample imbalance problem, we propose an adaptive hard-sample focal loss. This loss can guide the model to reduce attention to easy-to-classify samples and outliers and to pay more attention to hard-to-classify samples. We conducted experiments on two public datasets, and the results proved the effectiveness of our method.

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Qu, Siyu, Duanbing Chen, and Dengyou Xu. "Document-level Relation Extraction Based on Heterogeneous Graph Convolutional Network and Local Semantic Fusion." Journal of Physics: Conference Series 2504, no. 1 (2023): 012004. http://dx.doi.org/10.1088/1742-6596/2504/1/012004.

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Abstract Document-level relation extraction is a fundamental task of many downstream applications such as knowledge graph and has gained improvement through document graph and sequence models. These methods always utilize the whole document as an essential global feature while ignoring the discrimination of entity representation. Focusing on local semantic feature, a novel model named GCNLEF based on graph convolutional network is proposed in this paper. In the presented method, a heterogeneous graph containing mention nodes and sentence group nodes is constructed first. Then multi-hop path reasoning is presented to infer the relations between entities. Experimental results on DocRED show that the proposed model can achieve 61.45 F 1 score with 90 epochs, improved by 0.15 F 1 score compared with the state-of-the-art method ATLOP.

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Sun, Qi, Tiancheng Xu, Kun Zhang, et al. "Dual-Channel and Hierarchical Graph Convolutional Networks for document-level relation extraction." Expert Systems with Applications 205 (November 2022): 117678. http://dx.doi.org/10.1016/j.eswa.2022.117678.

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Shi, Yong, Yang Xiao, Pei Quan, MingLong Lei, and Lingfeng Niu. "Document-level relation extraction via graph transformer networks and temporal convolutional networks." Pattern Recognition Letters 149 (September 2021): 150–56. http://dx.doi.org/10.1016/j.patrec.2021.06.012.

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Kuang, Hailan, Haoran Chen, Xiaolin Ma, and Xinhua Liu. "A Keyword Detection and Context Filtering Method for Document Level Relation Extraction." Applied Sciences 12, no. 3 (2022): 1599. http://dx.doi.org/10.3390/app12031599.

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Relation extraction (RE) is the core link of downstream tasks, such as information retrieval, question answering systems, and knowledge graphs. Most of the current mainstream RE technologies focus on the sentence-level corpus, which has great limitations in practical applications. Moreover, the previously proposed models based on graph neural networks or transformers try to obtain context features from the global text, ignoring the importance of local features. In practice, the relation between entity pairs can usually be inferred just through a few keywords. This paper proposes a keyword detection and context filtering method based on the Self-Attention mechanism for document-level RE. In addition, a Self-Attention Memory (SAM) module in ConvLSTM is introduced to process the document context and capture keyword features. By searching for word embeddings with high cross-attention of entity pairs, we update and record critical local features to enhance the performance of the final classification model. The experimental results on three benchmark datasets (DocRED, CDR, and GBA) show that our model achieves advanced performance within open and specialized domain relationship extraction tasks, with up to 0.87% F1 value improvement compared to the state-of-the-art methods. We have also designed experiments to demonstrate that our model can achieve superior results by its stronger contextual filtering capability compared to other methods.

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Li, Yuqing, and Xinhui Shao. "Biomedical document-level relation extraction with thematic capture and localized entity pooling." Journal of Biomedical Informatics 160 (December 2024): 104756. https://doi.org/10.1016/j.jbi.2024.104756.

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Jiang, Chao, Jinzhi Liao, Xiang Zhao, Daojian Zeng, and Jianhua Dai. "An adaptive confidence-based data revision framework for Document-level Relation Extraction." Information Processing & Management 62, no. 1 (2025): 103909. http://dx.doi.org/10.1016/j.ipm.2024.103909.

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Li, Yongfei, Yuanbo Guo, Chen Fang, Yongjin Hu, Yingze Liu, and Qingli Chen. "Feature-Enhanced Document-Level Relation Extraction in Threat Intelligence with Knowledge Distillation." Electronics 11, no. 22 (2022): 3715. http://dx.doi.org/10.3390/electronics11223715.

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Relation extraction in the threat intelligence domain plays an important role in mining the internal association between crucial threat elements and constructing a knowledge graph (KG). This study designed a novel document-level relation extraction model, FEDRE-KD, integrating additional features to take full advantage of the information in documents. The study also introduced a teacher–student model, realizing knowledge distillation, to further improve performance. Additionally, a threat intelligence ontology was constructed to standardize the entities and their relationships. To solve the problem of lack of publicly available datasets for threat intelligence, manual annotation was carried out on the documents collected from social blogs, vendor bulletins, and hacking forums. After training the model, we constructed a threat intelligence knowledge graph in Neo4j. Experimental results indicate the effectiveness of additional features and knowledge distillation. Compared to mainstream models SSAN, GAIN, and ATLOP, FEDRE-KD improved the F1score by 22.07, 20.06, and 22.38, respectively.

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Dong, Yihao, and Xiaolong Xu. "Relational distance and document-level contrastive pre-training based relation extraction model." Pattern Recognition Letters 167 (March 2023): 132–40. http://dx.doi.org/10.1016/j.patrec.2023.02.012.

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Liu, Bao, and Guilin Qi. "Document-level relation extraction with Double graph guidance for long-tailed distributions." Computers and Electrical Engineering 123 (April 2025): 110237. https://doi.org/10.1016/j.compeleceng.2025.110237.

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Yin, Yuan, Xuhua Ai, Yiting Yu, Xiaoye Qu, and Wei Wei. "Path-Aware Reasoning Network for document-level relation extraction with co-regularization loss." Knowledge-Based Systems 310 (February 2025): 112931. https://doi.org/10.1016/j.knosys.2024.112931.

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Zhong, Yu, Bo Shen, and Tao Wang. "Deconstructing reasoning paths and attending to semantic guidance for document-level relation extraction." Knowledge-Based Systems 301 (October 2024): 112328. http://dx.doi.org/10.1016/j.knosys.2024.112328.

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Han, Xiaoyu, and Lei Wang. "A Novel Document-Level Relation Extraction Method Based on BERT and Entity Information." IEEE Access 8 (2020): 96912–19. http://dx.doi.org/10.1109/access.2020.2996642.

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Zhang, Fu, Jiapeng Wang, Huangming Xu, Honglin Wu, Jingwei Cheng, and Weijun Li. "Mention Distance-aware Interactive Attention with Multi-step Reasoning for document-level relation extraction." Engineering Applications of Artificial Intelligence 141 (February 2025): 109746. https://doi.org/10.1016/j.engappai.2024.109746.

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Zhou, Yichao, Yu Yan, Rujun Han, et al. "Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 16 (2021): 14647–55. http://dx.doi.org/10.1609/aaai.v35i16.17721.

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There has been a steady need in the medical community to precisely extract the temporal relations between clinical events. In particular, temporal information can facilitate a variety of downstream applications such as case report retrieval and medical question answering. Existing methods either require expensive feature engineering or are incapable of modeling the global relational dependencies among the events. In this paper, we propose a novel method, Clinical Temporal ReLation Exaction with Probabilistic Soft Logic Regularization and Global Inference (CTRL-PG) to tackle the problem at the document level. Extensive experiments on two benchmark datasets, I2B2-2012 and TB-Dense, demonstrate that CTRL-PG significantly outperforms baseline methods for temporal relation extraction.

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Han, Ridong, Tao Peng, Benyou Wang, Lu Liu, Prayag Tiwari, and Xiang Wan. "Document-level relation extraction with relation correlations." Neural Networks, November 2023. http://dx.doi.org/10.1016/j.neunet.2023.11.062.

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Xu, Wang, Kehai Chen, and Tiejun Zhao. "Document-Level Relation Extraction with Path Reasoning." ACM Transactions on Asian and Low-Resource Language Information Processing, November 30, 2022. http://dx.doi.org/10.1145/3572898.

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Document-level relation extraction (DocRE) aims to extract relations among entities across multiple sentences within a document by using reasoning skills (i.e., pattern recognition, logical reasoning, coreference reasoning, etc.) related to the reasoning paths between two entities. However, most of the advanced DocRE models only attend to the feature representations of two entities to determine their relation, and do not consider one complete reasoning path from one entity to another entity, which may hinder the accuracy of relation extraction. To address this issue, this paper proposes a novel method to capture this reasoning path from one entity to another entity, thereby better simulating reasoning skills to classify relation between two entities. Furthermore, we introduce an additional attention layer to summarize multiple reasoning paths for further enhancing the performance of the DocRE model. Experimental results on a large-scale document-level dataset show that the proposed approach achieved a significant performance improvement on a strong heterogeneous graph-based baseline.

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Journal articles on the topic 'Document-level relation extraction'

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